Downlink CDMA signals transmitted over wireless channels are subject to fading and distortions due to multipath propagation. This is a well known problem that has attracted a great deal of attention. It is desirable for the receiver to be capable of undoing the channel distortions and recovering the transmitted signal subject to an optimality criterion. Those skilled in the art are aware that various approaches to compensate for distortion referred to as channel equalization is limited by practical considerations such as the amount of computing power that can be placed in the receiver and the time to carry out calculations.
Downlink CDMA receivers typically are a linear minimum mean squared error (LMMSE) equalizer, which performs the task of recovering the transmitted signal by minimizing the mean squared error between the transmitted signal and the estimated version of the signal.
To facilitate the discussion, we refer to FIG. 1. FIG. 1A, in the upper half of the figure depicts a prior art implementation of the block-adaptive LMMSE equalizer. In this conventional implementation, an equalizer value is computed exclusively from each block of data and is used to equalize the next block, e.g. the equalizer value for the (n+1)th block depends only on the nth block. For example, the data observed between points A and B are employed to evaluate the filter update that takes effect at time C.
The 90-chip delay between points B and C accounts for the computing time required by the hardware. The new filter is used to equalize the signal received after time C up to time E where the next filter update occurs. This implementation suffers from the “obsolescence” issue. In fast fading environments, the channel impulse response during the C-E interval is completely different from that during the A-B interval.
As a result, the equalizer designed during the current block becomes outdated for the next block. One way to resolve this problem could be to bring points A and E closer together so that they lie within a fraction of the channel's coherence time from each other. Doing so, however, shortens the block length and the equalizer estimate becomes unreliable.
Alternatively, we could, with the use of a data buffer, equalize each block by using the equalizer estimated from the same block. Unfortunately, this method introduces a demodulation delay that may exceed the maximum allowed by delay-sensitive applications such as voice transmission.
Without the one-block filtering delay, the block-adaptive LMMSE equalizer performs well and is widely accepted in the literature: I. Ghauri and D. T. M. Slock, “Linear receivers for the DS-CDMA downlink exploiting orthogonality of spreading sequences,” in Proc. 32nd Asilomar Conf. Signals, Systems, Computers, vol. 1, pp. 6506-654, 1-4 Nov. 1998 [1]; T. P. Krauss, W. J. Hillery, and M. D. Zoltowsky, “MMSE equalization for forward link in 3G CDMA: symbol-level versus chip-level,” Tenth IEEE workshop on Stat. Signal and Array Proc., 2000.[2]; J. Zhang, T. Bhatt, and G. Mandyam, “Efficient linear equalization for high data rate downlink CDMA signaling,” 37th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, Calif. 9-12 Nov. 2003.[3]; T. P. Krauss, M. D. Zoltowski, and G. Leus, “Simple MMSE equalizers for CDMA downlink to restore chip sequence: comparison to zero-forcing and RAKE,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 5, pp. 2865-2868, 5-9 Jun. 2000 [4]. In the following discussion, numerals in square brackets [i] relate to references and numerals in parentheses relate to equations (n).
However, when the delay is present, the equalizer performance becomes severely limited and this issue has been given little attention. Note that filtering delay here refers to the time difference between the newest data point contributing to the current equalizer value and the newest data point that gets passed through that equalizer. For example, with reference to paragraphs [0004] and [0005], point B in FIG. 1A is the newest data point that contributes to the filter that takes effect at point C in FIG. 1A; point E in FIG. 1A is the newest data point that gets passed through this filter; thus, the time elapsed between points B and E represents the filtering delay. Demodulation delay here means the time difference between the newest equalized data point and the newest un-equalized data point at the receiver input at any instant; this delay essentially indicates the amount of un-equalized backlog data before the equalizer.
In previous work, e.g. [1], [2], [3], [4], the block-adaptive LMMSE equalizer was implemented with a data buffer to avoid the filtering delay. As described above, each data block is stored in a buffer while the equalizer is being synthesized from this data. When the synthesis is completed, the data is pushed out of the buffer and passed through that newly synthesized equalizer. This method introduces too large a demodulation delay that may be unacceptable for certain applications such as live voice transmission. The demodulation delay can be reduced by allowing a filtering delay or by shortening the block size. Unfortunately, this scheme reduces the adaptive capability of the LMMSE equalizer under high mobility conditions, or decreases the reliability of the filter estimate due to shrunken sample size.